Machine learning (ML) has exhibited great potential to transform the computing field. Huge interest has developed over the past years in applying machine learning-assisted approaches in the Internet of Things, healthcare, transportation, and security space, to name a few. However, the assumption in many current solutions is that big training data is widely available and transferable to a centralized server without much considering data privacy concerns. A new framework for machine learning has emerged, referred to as Federated Learning (FL), that advocates the AI-on-edge principle. The main objective of federated learning is to provide privacy-by-design training with decentralized data among local machines at the edge layer. In federated learning, a central server just coordinates with local clients to aggregate the model's updates without requiring the actual data (i.e., zero-touch). However, given the fresh nature of the FL, it is important to keep improving and design innovative solutions to mitigate its shortcomings and identify its best applications. This is the focus of the 'Decentralized Federated Learning: Applications, Solutions, and Challenges' mini-track. In this introduction article, we will describe the topic and the accepted paper(s) contributed by researchers.